Randomized controlled trials

Tài liệu về Randomized controlled trials

Nội dung

Sách

• Angrist, J. D., & Pischke, J. S. (2014). Mastering'metrics: The path from cause to effect. Princeton university press. (Chapter 1)

• Cunningham, S. (2021). Causal Inference. The Mixtape. Yale University Press. (Chapter 4)

• Imbens, G. W., & Rubin, D. B. (2015). Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge University Press. (Chapter 1 -3, 7)

• Murnane, R. J., & Willett, J. B. (2010). Methods Matter: Improving Causal Inference in Educational

• and Social Science Research. Oxford University Press. (Chapter 4 - 5)

Bài báo

• Angelucci, M., & De Giorgi, G. (2009). Indirect effects of an aid program: how do cash transfers affect ineligibles' consumption?. American economic review, 99(1), 486-508.

• Aron-Dine, A., Einav, L., & Finkelstein, A. (2013). The RAND health insurance experiment, three decades later. Journal of Economic Perspectives, 27(1), 197-222.

• Athey, S., & Imbens, G. W. (2017). The econometrics of randomized experiments. In Handbook of economic field experiments (Vol. 1, pp. 73-140). North-Holland.

• Banerjee, A. V., & Duflo, E. (2009). The experimental approach to development economics. Annu. Rev. Econ., 1(1), 151-178.

• Chetty, R., Hendren, N., & Katz, L. F. (2016). The effects of exposure to better neighborhoods on children: New evidence from the moving to opportunity experiment. American Economic Review, 106(4), 855-902.

• Deaton, A., & Cartwright, N. (2018). Understanding and misunderstanding randomized controlled trials. Social science & medicine, 210, 2-21.

• Cox, D. R. (1992). Causality: some statistical aspects. Journal of the Royal Statistical Society: Series A (Statistics in Society), 155(2), 291-301.

• Heckman, J. J. (1991). Randomization and social policy evaluation. Cambridge, MA: National Bureau of Economic Research.

• Heckman, J. J., & Smith, J. A. (1995). Assessing the Case for Social Experiments. Journal of Economic Perspectives, 9(2), 85-110.

• Holland, P. (1986). Statistics and Causal Inference. Journal of the American Statistical Association, 81(396), 945-960.

• Hudgens, M. G., & Halloran, M. E. (2008). Toward Causal Inference with Interference. Journal of the American Statistical Association, 103(482), 832-842.

• Kling, J. R., Ludwig, J., & Katz, L. F. (2005). Neighborhood Effects on Crime for Female and Male Youth: Evidence from a Randomized Housing Voucher Experiment. The Quarterly Journal of Economics, 120(1), 87-130.

• Manski, C. F. (2013). Identification of Treatment Response with Social Interactions. The Econometrics Journal, 16(1), S1-S23.

• Miguel, E., & Kremer, M. (2004). Worms: Identifying Impacts on Education and Health in the Presence of Treatment Externalities. Econometrica, 72(1), 159-217.

• Parker, S. W., & Todd, P. E. (2017). Conditional Cash Transfers: The Case of Progresa/ Oportunidades. Journal of Economic Literature, 55(3), 866-915.

• Ravallion, M. (2018). Should the Randomistas (Continue to) Rule?. Center for Global Development Working Paper, 492.

• Rubin, D. B. (1974). Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies. Journal of Educational Psychology, 66(5), 688.

• Sobel, M. E. (2006). What Do Randomized Studies of Housing Mobility Demonstrate? Causal Inference in the Face of Interference. Journal of the American Statistical Association, 101(476), 1398- 1407.

• Słoczynski, T. (2020). Interpreting ols estimands when treatment e ´ ffects are heterogeneous: Smaller groups get larger weights. The Review of Economics and Statistics, 1-27.

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